Can a machine write good poetry? Any positive answer raises fundamental questions about the nature and value of art. We report a seven-month poetry workshop in which a large language model was shaped into a digital poet through iterative in-context expert feedback, without retraining. Across sessions, the model developed a distinctive style and a coherent corpus, supported by quantitative and qualitative analyses, and it produced a pen name and author image. In a blinded authorship test with 50 humanities students and graduates (three AI poems and three poems by well-known poets each), judgments were at chance: human poems were labeled human 54% of the time and AI poems 52%, with 95% confidence intervals including 50%. After the workshop, a commercial publisher released a poetry collection authored by the model. These results show that workshop-style prompting can support long-horizon creative shaping and renew debates on creativity and authorship.

翻译：机器能否创作出优秀的诗歌？任何肯定的回答都会引发关于艺术本质与价值的根本性问题。本文报告了一项为期七个月的诗歌工作坊，通过迭代式的上下文专家反馈（无需重新训练），将一个大语言模型塑造为数字诗人。在多次工作坊活动中，该模型形成了独特的风格和连贯的作品体系，定量与定性分析均支持这一结论，并且模型还生成了笔名和作者形象。在一项包含50名人文专业学生及毕业生参与的盲审作者归属测试中（每人审阅三首AI诗歌和三首知名诗人作品），判断结果处于随机水平：人类诗歌被标注为人类创作的比例为54%，AI诗歌被标注为AI创作的比例为52%，95%置信区间均包含50%。工作坊结束后，一家商业出版社出版了由该模型创作的诗集。这些结果表明，工作坊式的提示工程能够支持长周期的创造性塑造，并重新引发关于创造力与作者身份的讨论。